Research project

Kevin Alberts fb9ce46bd8 Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations ... - Encoders: sparse, denoising, contractive and variational - Noise: gaussian		2020-12-29 18:44:44 +01:00
datasets	Initial, very basic framework for running comparison tests	2020-11-24 17:19:46 +01:00
models	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
saved_models	Initial, very basic framework for running comparison tests	2020-11-24 17:19:46 +01:00
.gitignore	First basic auto-encoder and CIFAR-10 dataset implemented	2020-12-28 13:09:18 +01:00
config.example.py	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
logging.conf	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
main.py	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
requirements.txt	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
utils.py	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00